Gain adjustment is important for ultrasound imaging. Correspondingly, ultrasound imaging systems may be generally provided with a fixed gain that is suitable for a majority of subjects. Since ultrasonic waves may be attenuated to a different extent within each patient, however, the fixed gain predetermined in the systems may not be suitable in all cases. At this point, users may need to manually adjust time gain compensation (TGC), lateral gain compensation (LGC) and a master gain. Such manual adjustment is time-consuming, and an optimal gain curve may not be obtained in this case, where automatic gain optimization can overcome these drawbacks and automatic gain optimization function may thus be equipped for many ultrasound imaging systems. However, automatic gain optimization is mostly applied to B-mode ultrasound images, and most of the methods for automatically optimizing the gain may obtain a gain compensation curve by counting image brightness and noise information and then calculating differences between target brightness and brightness of a tissue region.
Contrast imaging, overcomes various drawbacks of conventional imaging, such as not being able to display blood flow and/or not being able to display micro-vessels. The contrast imaging may obtain and display blood flow information and vessel distribution within a human body using a non-linear detection technology to detect some micro-bubbles that may have a size similar to blood cells and be injected into the human body. The contrast imaging may be generally operated under a low mechanical index, so that the micro-bubbles can be prevented from being destroyed by sound waves. Due to the low mechanical index, emitted energy for the contrast imaging may also be low, and thus a signal to noise ratio (SNR) of the contrast imaging may be lower than that of the conventional B-mode ultrasound image. Besides, it is difficult to apply the method for optimizing the gain of the conventional B-mode ultrasound image to the contrast image. Moreover, as a concentration of the micro-bubbles changes within the human body before or after injecting a contrast agent, brightness of a contrast image may vary correspondingly. In this case, the conventional methods for automatically optimizing the gain by counting image brightness cannot be applied to the contrast image.
Some conventional methods for automatically optimizing the gain of the contrast image may be based on the relation between the brightness of the contrast image and the contrast agent. When the image brightness is greater than a preset value or an amount of the contrast agent reaches a preset value, the contrast image may be processed by the method for automatically optimizing the gain of the conventional B-mode images; alternatively, the brightness may be adjusted according to noise information. However, this may lead to different brightness compensation for a same patient since different optimization methods are started at each stage.
The contrast imaging can often generate two kinds of images: a tissue image representing tissue information and a contrast image representing contrast agent information, where a same gain curve may be applied to those two images up to now. Due to different characteristics of the contrast image and the tissue image, however, the gain curve set according to the tissue image may not be suitable for the contrast image. For example, brightness of the contrast image may vary before the micro-bubbles are injected and as the micro-bubbles get enhanced or decreased; and/or the contrast image and the tissue image may have different attenuation characteristics. Therefore, different gain curves may be required according to different image characteristics.